کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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1145402 | 1489663 | 2015 | 22 صفحه PDF | دانلود رایگان |
Let (εj)j≥0(εj)j≥0 be a sequence of independent pp-dimensional random vectors and τ≥1τ≥1 a given integer. From a sample ε1,…,εT+τε1,…,εT+τ of the sequence, the so-called lag-ττ auto-covariance matrix is Cτ=T−1∑j=1Tετ+jεjt. When the dimension pp is large compared to the sample size TT, this paper establishes the limit of the singular value distribution of CτCτ assuming that pp and TT grow to infinity proportionally and the sequence has uniformly bounded (4+δ)(4+δ)th order moments. Compared to existing asymptotic results on sample covariance matrices developed in random matrix theory, the case of an auto-covariance matrix is much more involved due to the fact that the summands are dependent and the matrix CτCτ is not symmetric. Several new techniques are introduced for the derivation of the main theorem.
Journal: Journal of Multivariate Analysis - Volume 137, May 2015, Pages 119–140